1. Field
The present disclosure relates to a method for optical character recognition of hand-printed, hand-written, and printed characters. Said method can also be used for recognition of any pre-defined objects in an image.
2. Related Art
There are many known image recognition methods that involve a comparison of an input image in the form of initial image units that are aggregated (usually pixels) with a model image of the whole object or a set of possible embodiments of the object stored in a special reference, usually termed a “classifier.”
A known group of character recognition methods involve parsing the document into parts presumably containing images of letters followed by further comparison of said images with those stored in one or more special feature and/or raster classifiers.
Another known group of character recognition methods uses structural character descriptions for optical recognition. Two well known, and substantially different, approaches in this group are a linguistic approach and a fuzzy graph matching approach.
The linguistic approach consists of creating a language with a set of rules which are used to direct a search for local image properties and segmenting the image. The contents of individual segments, their positions relative to one another, etc. are then analyzed. The basic features include “corner,” “cross,” “vertex”, and others, which are basic enough to be encountered in many images of one class. Each class of images is associated with a set of rules that describes the given class (e.g. characters, graphic objects) in terms of the selected basic features.
The fuzzy graph matching approach represents character images as graphs. The recognition process in this case consists of finding the minimum transform that transforms the graph of the character to be recognized into a pattern graph.
Existing structural classifiers have significant drawbacks. They compute image features and attempt to represent the object under recognition as a collection of structural elements. As a result, the classifiers must analyze and interpret complex combinations of elements that are not always described in the classifier patterns, and the classifiers must go through multiple possible variants, which greatly slows down the recognition process.
The present invention solves these and other drawbacks in the art by making the recognition of objects more reliable and providing an increased immunity to noise.